Informatica

We attempted to determine the most common localizations of epileptogenic foci by using common functional (EEG and PET/CT) and structural (MRI) imaging methods. Also, we compared the number of epileptogenic foci detected with all diagnostic methods and determined the success rate of surgery in the operated patients when the epileptogenic foci coincided on all three imaging methods. 35 patients (including children) with clinically proven refractory epilepsy were included into the study. All patients underwent an MRI scan with epilepsy protocol, Fluorodeoxyglucose-18-PET/CT scan, and an EEG prior to a PET study. 14 patients underwent neurosurgery for removal of epileptogenic foci. We found a statistically significant difference between the number of epileptogenic foci which were found in PET/CT and EEG studies but there was no significant difference between MRI and PET/CT lesion numbers. The most common localization of epileptogenic activity on EEG was right temporal lobe (54.3%); the most common lobe with structural changes on MRI was right temporal lobe (42.9%); the most common hypometabolism zone on PET/CT was in right temporal lobe (45.7%). 10 out of 14 patients who underwent surgery demonstrated excellent postsurgical outcomes, with no epileptic seizures one year or more after the operation; 3/14 patients had 1–2 seizures after surgery and one patient had the same count or more epileptic seizures in duration of one year or more. The measure of Agreement Kappa between PET/CT and EEG value was 0.613 $(p<0.05)$. Between PET/CT and MRI the value was 0.035 $(p>0.05)$. Surgical treatment may offer hope for patients with intractable epileptic seizures. PET/CT was an extremely useful imaging method to assist in the localization of epileptogenic zones. The dynamic functional information that brain PET/CT provides is complementary to anatomical imaging of MRI and functional information of EEG.

This paper describes a preliminary algorithm performing epilepsy prediction by means of visual perception tests and digital electroencephalograph data analysis. Special machine learning algorithm and signal processing method are used. The algorithm is tested on real data of epileptic and healthy persons that are treated in Kaunas Medical University Clinics, Lithuania. The detailed examination of results shows that computerized visual perception testing and automated data analysis could be used for brain damages diagnosing.